Summary of Differentially Private Post-processing For Fair Regression, by Ruicheng Xian et al.
Differentially Private Post-Processing for Fair Regression
by Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a differentially private post-processing algorithm for learning fair regressors that satisfy statistical parity. The algorithm addresses privacy concerns by protecting sensitive data and fairness concerns by preventing historical biases from being propagated. It consists of three steps: estimating output distributions privately, computing the Wasserstein barycenter, and using optimal transports to post-process outputs. The sample complexity is analyzed, revealing a trade-off between statistical bias and variance depending on the choice of bins in the histogram. This approach favors fairness over error when fewer bins are used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine learning models fairer by protecting sensitive data from being abused. It’s like a special filter that takes any model and makes it more balanced, so it doesn’t unfairly favor or disadvantage certain groups. The algorithm has three parts: first, it quietly figures out what the model might do, then it calculates where this output should be shifted to make it fair, and finally, it uses special techniques to adjust the model’s outputs. The researchers show that this approach is reliable and can even balance fairness with accuracy by using fewer or more bins. |
Keywords
» Artificial intelligence » Machine learning
